• Abstract

    As indústrias de manufatura, química e de processo frequentemente empregam controladores lógicos programáveis (PLCs) em sistemas de automação industrial porque são muito eficazes e confiáveis em aplicações que exigem controle sequencial e sincronização de processos e peças auxiliares. Apesar de sua robustez, idealismo e tolerância a condições operacionais adversas, como ar impuro, umidade, vibração e ruído elétrico, os sistemas de controle baseados em PLC podem, no entanto, funcionar mal, levando a uma quantidade substancial de tempo de inatividade. Para determinar os métodos que foram empregados com mais frequência para aumentar a confiabilidade de sistemas ou componentes de 2010 a 2023, este estudo oferecerá tendências na análise de confiabilidade de sistemas. Para revisar artigos publicados nos últimos 14 anos, este estudo utilizou um processo sistemático de revisão da literatura. A análise de confiabilidade foi dividida em categorias. Os resultados indicaram que, embora o uso de modelos combinatórios e híbridos esteja em ascensão, a modelagem combinatória, que é o processo de criação de um modelo matemático para resolver um problema, pode ser usada para explicar essa tendência crescente. Os modelos híbridos, que usam soluções combinatórias e baseadas no espaço de estados, são comumente considerados os métodos mais avançados para avaliar a confiabilidade. Para análise de confiabilidade, modelos de espaço de estados têm sido utilizados com cada vez mais frequência. Este estudo ajudará outros pesquisadores a descobrir as lacunas na análise de confiabilidade que precisam ser preenchidas para escolher o melhor curso de ação para novas pesquisas.

  • References

    1. Abdelrahman, O., & Keikhosrokiani, P. (2020). Assembly line anomaly detection and root cause analysis using machine learning. IEEE Access, 8, 189661–189672. https://doi.org/10.1109/ACCESS.2020.3029826
    2. Afefy, I. H. A., Mohib, A., El-kamash, A. M., & Mahmoud, M. A. (2019). A new framework of reliability centered maintenance. Jordan Journal of Mechanical and Industrial Engineering, December.
    3. Ahmad, N. (2017). A framework for application of reliability centered maintenance in the lead oxide production system. Applied Mechanics and Materials, December 2016. https://doi.org/10.4028/www.scientific.net/AMM.860.123
    4. Ali, N. G., Mokhtarei, A., Khodayarei, A., & Ataei, M. (2014). Fault tree analysis of failure cause of crushing plant and mixing bed hall at Khoy cement factory in Iran. Case Studies in Engineering Failure Analysis, 2(1), 33–38. https://doi.org/10.1016/j.csefa.2013.12.006
    5. Aquitas Solutions. (2018). Mean time between failure (MTBF) & mean time to repair (MTTR): Simplifying the approach to two key metrics in maintenance.
    6. Aydogmus, O., & Talu, M. F. (2012). A vision-based measurement installation for programmable logic controllers. Measurement: Journal of the International Measurement Confederation, 45(5), 1098–1104. https://doi.org/10.1016/j.measurement.2012.01.031
    7. Barsalou, M. (2023). Root cause analysis in Quality 4.0: A scoping review of current state and perspectives. TEM Journal, 12(1), 73–79. https://doi.org/10.18421/TEM121-10
    8. Bartelt, T. (1997). Industrial electronics devices, systems and applications. Delmar Publishers.
    9. Bayindir, R., & Cetinceviz, Y. (2011). A water pumping control system with a programmable logic controller (PLC) and industrial wireless modules for industrial plants: An experimental setup. ISA Transactions, 50(2), 321–328. https://doi.org/10.1016/j.isatra.2010.10.006
    10. Benidris, M., Elsaiah, S., & Mitra, J. (2015). Power system reliability evaluation using a state space classification technique and particle swarm optimisation search method. IET Generation, Transmission & Distribution, 9, 1865–1873. https://doi.org/10.1049/iet-gtd.2015.0581
    11. Bensaci, C., Zennir, Y., Pomorski, D., Innal, F., & Lundteigen, M. A. (2023). Collision hazard modeling and analysis in a multi-mobile robots system transportation task with STPA and SPN. Reliability Engineering & System Safety, 234, 109138. https://doi.org/10.1016/j.ress.2023.109138
    12. Berrell, R., & Chakrabortty, R. K. (2022). Improving the availability of Australian hospitals’ critical medical devices. IFAC-PapersOnLine, 55(10), 1278–1283. https://doi.org/10.1016/j.ifacol.2022.09.566
    13. Bhamu, J., & Sangwan, K. S. (2014). Lean manufacturing: Literature review and research issues. International Journal of Operations & Production Management, 34(7), 876–940. https://doi.org/10.1108/IJOPM-08-2012-0315
    14. Bhattacharyya, S. K., & Cheliyan, A. S. (2019). Optimization of a subsea production system for cost and reliability using its fault tree model. Reliability Engineering & System Safety, 185, 213–219. https://doi.org/10.1016/j.ress.2018.12.030
    15. Bi, W., Chen, W., & Pan, J. (2022). Multidisciplinary reliability design considering hybrid uncertainty incorporating deep learning. Wireless Communications and Mobile Computing, 2022. https://doi.org/10.1155/2022/5846684
    16. Bindi, M., Piccirilli, M. C., Luchetta, A., Grasso, F., & Manetti, S. (2022). Testability evaluation in time-variant circuits: A new graphical method. Electronics (Switzerland), 11(10). https://doi.org/10.3390/electronics11101589
    17. Carnevali, L., Ciani, L., Fantechi, A., Gori, G., & Papini, M. (2021). An efficient library for reliability block diagram evaluation. Applied Sciences (Switzerland), 11(9). https://doi.org/10.3390/app11094026
    18. Carpitella, S., & Certa, A. (2018). A combined multi-criteria approach to support FMECA analyses: A real-world case. Reliability Engineering and System Safety, 169, 394–402. https://doi.org/10.1016/j.ress.2017.09.017
    19. Chen, Y., Li, Y., Kang, R., & Ali, M. (2020). Reliability analysis of PMS with failure mechanism accumulation rules and a hierarchical method. Reliability Engineering & System Safety, 197, 106774. https://doi.org/10.1016/j.ress.2019.106774
    20. Cheng, Y., Elsayed, E. A., & Chen, X. (2021). Random multi-hazard resilience modeling of engineered systems and critical infrastructure. Reliability Engineering and System Safety, 209, 107453. https://doi.org/10.1016/j.ress.2021.107453
    21. Childs, L., Jenab, K., & Moslehpour, S. (2018). A Petri net based reliability block diagram model for category I medical devices reliability analysis. Management Science Letters, 8(11), 1159–1168. https://doi.org/10.5267/j.msl.2018.8.008
    22. Choi, S. H. (2014). Reliability and availability analysis of process systems under changing operating conditions. In Computer Aided Chemical Engineering (Vol. 33, pp. 1135–1138). Elsevier. https://doi.org/10.1016/B978-0-444-63455-9.50108-2
    23. Chookah, M., Nuhi, M., & Modarres, M. (2011). A probabilistic physics-of-failure model for prognostic health management of structures subject to pitting and corrosion-fatigue. Reliability Engineering and System Safety, 96(12), 1601–1610. https://doi.org/10.1016/j.ress.2011.07.007
    24. Da Silva Neto, A. V., Vismari, L. F., Gimenes, R. A. V., Sesso, D. B., De Almeida, J. R., Cugnasca, P. S., & Camargo, J. B. (2018). A practical analytical approach to increase confidence in PLD-based systems safety analysis. IEEE Systems Journal, 12(4), 3473–3484. https://doi.org/10.1109/JSYST.2017.2726178
    25. David, L., Král, V., Stanislav, R., & Radomír, G. (2017). Software solution design for application of reliability centered maintenance in preventive maintenance plan. Proceedings of the 2017 18th International Scientific Conference on Electric Power Engineering (EPE). https://doi.org/10.1109/EPE.2017.7967354
    26. De Andrade, M. A. H., de Carvalho Michalski, M. A., da Silva, R. F., Netto, A. C., & de Souza, G. F. M. (2022). Petri net-based reliability and availability analysis to support asset management: A CODOG propulsion system case study. IFAC-PapersOnLine, 55(19), 19–24. https://doi.org/10.1016/j.ifacol.2022.09.178
    27. De Vasconcelos, V., Soares, W. A., & Marques, R. O. (2018). Integrated engineering approach to safety, reliability, risk management and human factors. In Springer Series in Reliability Engineering. https://doi.org/10.1007/978-3-319-62319-1_4
    28. Deepak, P. P., & Jagathy, R. V. P. (2015). An evaluation of alternative approaches to reliability centered maintenance. International Journal of Applied Engineering Research, 10(19), 40350–40359.
    29. Denyer, D., & Tranfield, D. (2009). Producing a systematic review. In D. A. Buchanan & A. Bryman (Eds.), The SAGE handbook of organizational research methods (pp. 671–689). SAGE Publications Ltd.
    30. Dhillon, B. S. (2006). Maintainability, maintenance, and reliability for engineers. CRC Press. https://doi.org/10.1201/9781420006780
    31. Di, P., Wang, X., Chen, T., & Hu, B. (2020). Multisensor data fusion in testability evaluation of equipment. Mathematical Problems in Engineering, 2020. https://doi.org/10.1155/2020/7821070
    32. Diego, P., Bíscaro, A. A. P., Leão, F. B., & Mantovani, R. S. (2016). A new approach for reliability-centered maintenance programs in electric power distribution systems based on a multiobjective genetic algorithm. Electric Power Systems Research, 137, 41–50. https://doi.org/10.1016/j.epsr.2016.03.040
    33. Ding, L., Wang, H., Jiang, J., & Xu, A. (2017). SIL verification for SRS with diverse redundancy based on system degradation using reliability block diagram. Reliability Engineering and System Safety, 165, 170–187. https://doi.org/10.1016/j.ress.2017.03.005
    34. Ding, R., Liu, Z., Xu, J., Meng, F., Sui, Y., & Men, X. (2021). A novel approach for reliability assessment of residual heat removal system for HPR1000 based on failure mode and effect analysis, fault tree analysis, and fuzzy Bayesian network methods. Reliability Engineering & System Safety, 216, 107911. https://doi.org/10.1016/j.ress.2021.107911
    35. Ding, S. H., & Kamaruddin, S. (2015). Maintenance policy optimization—literature review and directions. International Journal of Advanced Manufacturing Technology, 76(5–8), 1263–1283. https://doi.org/10.1007/s00170-014-6341-2
    36. Distefano, S., Longo, F., & Trivedi, K. S. (2012). Investigating dynamic reliability and availability through state–space models. Computers & Mathematics with Applications, 64(12), 3701–3716. https://doi.org/10.1016/j.camwa.2012.02.038
    37. Doostparast, M., Kolahan, F., & Doostparast, M. (2014). A reliability-based approach to optimize preventive maintenance scheduling for coherent systems. Reliability Engineering and System Safety, 126, 98–106. https://doi.org/10.1016/j.ress.2014.01.010
    38. Drost, E. (2011). Validity and reliability in social science research. Education Research and Perspectives, 38(1). https://search.informit.org/doi/abs/10.3316/INFORMIT.491551710186460
    39. Edwin, K. (2019). Reliability and validity of research instruments. In Critical analysis of policies on special education in Kenya.
    40. Elusakin, T., Shafiee, M., Adedipe, T., & Dinmohammadi, F. (2021). A stochastic petri net model for O&M planning of floating offshore wind turbines. Energies, 14(4), 1–18. https://doi.org/10.3390/en14041134
    41. Elsevier. (n.d.). ScienceDirect. https://www.sciencedirect.com/ (Accessed June 2023)
    42. Engelmann, A., & Jukan, A. (2021). A combinatorial reliability analysis of generic service function chains in data center networks. ACM Transactions on Modeling and Performance Evaluation of Computing Systems, 6(3), 1–28. https://doi.org/10.1145/3477046
    43. Fan, L., Su, H., Wang, W., Zio, E., Zhang, L., Yang, Z., Peng, S., Yu, W., Zuo, L., & Zhang, J. (2022). A systematic method for the optimization of gas supply reliability in natural gas pipeline network based on Bayesian networks and deep reinforcement learning. Reliability Engineering & System Safety, 225, 108613. https://doi.org/10.1016/j.ress.2022.108613
    44. Fu, G., Su, Y., Guo, W., Wan, B., Zhang, Z., & Wang, Y. (2018). Life prediction methodology of system-in-package based on physics of failure. Microelectronics Reliability, 88–90, 173–178. https://doi.org/10.1016/j.microrel.2018.06.119
    45. Fuentes-Huerta, M. A., González-González, D. S., Cantú-Sifuentes, M., & Praga-Alejo, R. J. (2021). Fuzzy reliability centered maintenance considering personnel experience and only censored data. Computers and Industrial Engineering, 158, 1–6. https://doi.org/10.1016/j.cie.2021.107440
    46. Gao, H., Cui, L., & Kong, D. (2018). Reliability analysis for a Wiener degradation process model under changing failure thresholds. Reliability Engineering and System Safety, 171, 1–8. https://doi.org/10.1016/j.ress.2017.11.006
    47. Gao, H., Cui, L., & Yi, H. (2019). Availability analysis of k-out-of-n: F repairable balanced systems with m sectors. Reliability Engineering and System Safety, 191, 106572. https://doi.org/10.1016/j.ress.2019.106572
    48. Gao, S., & Wang, J. (2021). Reliability and availability analysis of a retrial system with mixed standbys and an unreliable repair facility. Reliability Engineering and System Safety, 205, 107240. https://doi.org/10.1016/j.ress.2020.107240
    49. Garousi, V., Felderer, M., & Kılıçaslan, F. N. (2019). A survey on software testability. Information and Software Technology, 108, 35–64. https://doi.org/10.1016/j.infsof.2018.12.003
    50. Ghadhab, M., Junges, S., Katoen, J.-P., Kuntz, M., & Volk, M. (2019). Safety analysis for vehicle guidance systems with dynamic fault trees. Reliability Engineering & System Safety, 186, 37–50. https://doi.org/10.1016/j.ress.2019.02.005
    51. Ghazizadeh, P., Florin, R., Zadeh, A. G., & Olariu, S. (2016). Reasoning about mean time to failure in vehicular clouds. IEEE Transactions on Intelligent Transportation Systems, 17(3), 751–761. https://doi.org/10.1109/TITS.2015.2486523
    52. Giannakoulopoulos, A., Pergantis, M., & Konstantinou, N. (2020). Exploring the dominance of the English language on the websites of EU countries. Future Internet, 12(4), 76. https://doi.org/10.3390/fi12040076
    53. Gilardoni, G. L., De Toledo, M. L. G., Freitas, M. A., & Colosimo, E. A. (2016). Dynamics of an optimal maintenance policy for imperfect repair models. European Journal of Operational Research, 248(3), 1104–1112. https://doi.org/10.1016/j.ejor.2015.07.056
    54. Grantham Lough, K. A., Stone, R. B., & Tumer, I. Y. (2008). Failure prevention in design through effective catalogue utilization of historical failure events. Journal of Failure Analysis and Prevention, 8(5), 469–481. https://doi.org/10.1007/s11668-008-9160-7
    55. Hasan, O., Ahmed, W., Tahar, S., & Hamdi, M. S. (2015). Reliability block diagrams based analysis: A survey. AIP Conference Proceedings, 1648, March. https://doi.org/10.1063/1.4913184
    56. He, Y., Gu, C., Chen, Z., & Han, X. (2017). Integrated predictive maintenance strategy for manufacturing systems by combining quality control and mission reliability analysis. International Journal of Production Research, 55(19), 5841–5862. https://doi.org/10.1080/00207543.2017.1346843
    57. Heale, R., & Twycross, A. (2015). Validity and reliability in quantitative studies. Evidence-Based Nursing, 18(3), 66–67. https://doi.org/10.1136/eb-2015-102129
    58. Hendricks, C., George, E., Osterman, M., & Pecht, M. (2015). Physics-of-failure (PoF) methodology for electronic reliability. In J. Swingler (Ed.), Reliability characterization of electrical and electronic systems (pp. 47–68). Woodhead Publishing. https://doi.org/10.1016/B978-1-78242-221-1.00003-4
    59. Hong, L., Li, H., Fu, J., Li, J., & Peng, K. (2022). Hybrid active learning method for non-probabilistic reliability analysis with multi-super-ellipsoidal model. Reliability Engineering & System Safety, 222, 108414. https://doi.org/10.1016/j.ress.2022.108414
    60. Hong, L., Li, H., Fu, J., Li, J., & Peng, K. (2022). Hybrid active learning method for non-probabilistic reliability analysis with multi-super-ellipsoidal model. Reliability Engineering & System Safety, 222, 108414. https://doi.org/10.1016/j.ress.2022.108414
    61. Hu, Y., Peng, Q., Ni, Q., Wu, X., & Ye, D. (2023). Event-based safety and reliability analysis integration in model-based space mission design. Reliability Engineering & System Safety, 229, 108866. https://doi.org/10.1016/j.ress.2022.108866
    62. Hu, Y., Peng, Q., Ni, Q., Wu, X., & Ye, D. (2023). Event-based safety and reliability analysis integration in model-based space mission design. Reliability Engineering & System Safety, 229, 108866. https://doi.org/10.1016/j.ress.2022.108866
    63. Huhn, S., & Drechsler, R. (2021). Design for testability, debug and reliability: Next generation measures using formal techniques. Springer. https://doi.org/10.1007/978-3-030-69209-4
    64. Huhn, S., & Drechsler, R. (2021). Design for testability, debug and reliability: Next generation measures using formal techniques. Springer. https://doi.org/10.1007/978-3-030-69209-4
    65. Ishimatsu, T., Leveson, N. G., Thomas, J. P., Fleming, C. H., Katahira, M., Miyamoto, Y., Ujiie, R., Nakao, H., & Hoshino, N. (2014). Hazard analysis of complex spacecraft using systems-theoretic process analysis. Journal of Spacecraft and Rockets, 51(2), 509–522. https://doi.org/10.2514/1.A32449
    66. Ishimatsu, T., Leveson, N. G., Thomas, J. P., Fleming, C. H., Katahira, M., Miyamoto, Y., Ujiie, R., Nakao, H., & Hoshino, N. (2014). Hazard analysis of complex spacecraft using systems-theoretic process analysis. Journal of Spacecraft and Rockets, 51(2), 509–522. https://doi.org/10.2514/1.A32449
    67. Ito, A., Hagström, M., Bokrantz, J., Skoogh, A., Nawcki, M., Gandhi, K., Bergsjö, D., & Bärring, M. (2022). Improved root cause analysis supporting resilient production systems. Journal of Manufacturing Systems, 64(July), 468–478. https://doi.org/10.1016/j.jmsy.2022.07.015
    68. Ito, A., Hagström, M., Bokrantz, J., Skoogh, A., Nawcki, M., Gandhi, K., Bergsjö, D., & Bärring, M. (2022). Improved root cause analysis supporting resilient production systems. Journal of Manufacturing Systems, 64(July), 468–478. https://doi.org/10.1016/j.jmsy.2022.07.015
    69. Iyomi, E. P. (2021). Managing the integrity of mine cage conveyance using fault tree analysis. International Journal of Engineering Research & Technology, 10(06), 743–747.
    70. Iyomi, E. P. (2021). Managing the integrity of mine cage conveyance using fault tree analysis. International Journal of Engineering Research & Technology, 10(06), 743–747.
    71. Izadi, M., Sharafi, M., & Khaledi, B. E. (2018). New nonparametric classes of distributions in terms of mean time to failure in age replacement. Journal of Applied Probability, 55(4), 1238–1248. https://doi.org/10.1017/jpr.2018.82
    72. Izadi, M., Sharafi, M., & Khaledi, B. E. (2018). New nonparametric classes of distributions in terms of mean time to failure in age replacement. Journal of Applied Probability, 55(4), 1238–1248. https://doi.org/10.1017/jpr.2018.82
    73. Jia, L., Ren, Y., Yang, D., Feng, Q., Sun, B., & Qian, C. (2019). Reliability analysis of dynamic reliability block diagram based on dynamic uncertain causality graph. Journal of Loss Prevention in the Process Industries, 62, 103947. https://doi.org/10.1016/j.jlp.2019.103947
    74. Jia, L., Ren, Y., Yang, D., Feng, Q., Sun, B., & Qian, C. (2019). Reliability analysis of dynamic reliability block diagram based on dynamic uncertain causality graph. Journal of Loss Prevention in the Process Industries, 62, 103947. https://doi.org/10.1016/j.jlp.2019.103947
    75. Jiang, C., Han, X., Li, W. X., Liu, J., & Zhang, Z. (2012). A hybrid reliability approach based on probability and interval for uncertain structures. Journal of Mechanical Design, Transactions of the ASME, 134(3), 1–11. https://doi.org/10.1115/1.4005595
    76. Jiang, C., Han, X., Li, W. X., Liu, J., & Zhang, Z. (2012). A hybrid reliability approach based on probability and interval for uncertain structures. Journal of Mechanical Design, Transactions of the ASME, 134(3), 1–11. https://doi.org/10.1115/1.4005595
    77. Jiang, G. J., Li, Z. Y., Qiao, G., Chen, H. X., Li, H. Bin, & Sun, H. H. (2021). Reliability analysis of dynamic fault tree based on binary decision diagrams for explosive vehicle. Mathematical Problems in Engineering, 2021, 5559475. https://doi.org/10.1155/2021/5559475
    78. Jiang, G. J., Li, Z. Y., Qiao, G., Chen, H. X., Li, H. Bin, & Sun, H. H. (2021). Reliability analysis of dynamic fault tree based on binary decision diagrams for explosive vehicle. Mathematical Problems in Engineering, 2021, 5559475. https://doi.org/10.1155/2021/5559475
    79. Jung, S., Yoo, J., & Lee, Y. J. (2020). A practical application of NUREG/CR-6430 software safety hazard analysis to FPGA software. Reliability Engineering and System Safety, 202(May), 107029. https://doi.org/10.1016/j.ress.2020.107029
    80. Jung, S., Yoo, J., & Lee, Y. J. (2020). A practical application of NUREG/CR-6430 software safety hazard analysis to FPGA software. Reliability Engineering and System Safety, 202(May), 107029. https://doi.org/10.1016/j.ress.2020.107029
    81. Kabir, S. (2017). An overview of fault tree analysis and its application in model-based dependability analysis. Expert Systems with Applications, 77, 114–135. https://doi.org/10.1016/j.eswa.2017.01.058
    82. Kabir, S. (2017). An overview of fault tree analysis and its application in model-based dependability analysis. Expert Systems with Applications, 77, 114–135. https://doi.org/10.1016/j.eswa.2017.01.058
    83. Kaczor, G., Młynarski, S., & Szkoda, M. (2016). Verification of safety integrity level with the application of Monte Carlo simulation and reliability block diagrams. Journal of Loss Prevention in the Process Industries, 41, 31–39. https://doi.org/10.1016/j.jlp.2016.03.002
    84. Kaczor, G., Młynarski, S., & Szkoda, M. (2016). Verification of safety integrity level with the application of Monte Carlo simulation and reliability block diagrams. Journal of Loss Prevention in the Process Industries, 41, 31–39. https://doi.org/10.1016/j.jlp.2016.03.002
    85. Kang, J., Sun, L., & Guedes Soares, C. (2019). Fault tree analysis of floating offshore wind turbines. Renewable Energy, 133, 1455–1467. https://doi.org/10.1016/j.renene.2018.08.097
    86. Kang, J., Sun, L., & Guedes Soares, C. (2019). Fault tree analysis of floating offshore wind turbines. Renewable Energy, 133, 1455–1467. https://doi.org/10.1016/j.renene.2018.08.097
    87. Kattumannil, S. K., & Anisha, P. (2019). A simple non-parametric test for decreasing mean time to failure. Statistical Papers, 60(1), 73–87. https://doi.org/10.1007/s00362-016-0827-y
    88. Kattumannil, S. K., & Anisha, P. (2019). A simple non-parametric test for decreasing mean time to failure. Statistical Papers, 60(1), 73–87. https://doi.org/10.1007/s00362-016-0827-y
    89. Kaur, K. (2017). Improving reliability and testability using enhanced dynamic metrics. International Conference on Science, Technology and Management, 42–45.
    90. Kaur, K. (2017). Improving reliability and testability using enhanced dynamic metrics. International Conference on Science, Technology and Management, 42–45.
    91. Ke, J. C., Liu, T. H., & Yang, D. Y. (2018). Modeling of machine interference problem with unreliable repairman and standbys imperfect switchover. Reliability Engineering and System Safety, 174(January), 12–18. https://doi.org/10.1016/j.ress.2018.01.013
    92. Ke, J. C., Liu, T. H., & Yang, D. Y. (2018). Modeling of machine interference problem with unreliable repairman and standbys imperfect switchover. Reliability Engineering and System Safety, 174(January), 12–18. https://doi.org/10.1016/j.ress.2018.01.013
    93. Khanum, M. A., & Tripathi, A. M. (2015). An empirical study on testability measurement of object-oriented software. International Journal of Scientific & Engineering Research, 6(5), 1285–1290.
    94. Khanum, M. A., & Tripathi, A. M. (2015). An empirical study on testability measurement of object-oriented software. International Journal of Scientific & Engineering Research, 6(5), 1285–1290.
    95. Khatri, S. (2011). Improving the testability of object-oriented software during testing and debugging processes. International Journal of Computer Applications, 35(11), 24–35.
    96. Khatri, S. (2011). Improving the testability of object-oriented software during testing and debugging processes. International Journal of Computer Applications, 35(11), 24–35.
    97. Kleyner, A., & Volovoi, V. (2010). Application of Petri nets to reliability prediction of occupant safety systems with partial detection and repair. Reliability Engineering and System Safety, 95(6), 606–613. https://doi.org/10.1016/j.ress.2010.01.008
    98. Kleyner, A., & Volovoi, V. (2010). Application of Petri nets to reliability prediction of occupant safety systems with partial detection and repair. Reliability Engineering and System Safety, 95(6), 606–613. https://doi.org/10.1016/j.ress.2010.01.008
    99. Kuo, C. C., & Ke, J. C. (2016). Comparative analysis of standby systems with unreliable server and switching failure. Reliability Engineering and System Safety, 145, 74–82. https://doi.org/10.1016/j.ress.2015.09.001
    100. Kuo, C. C., & Ke, J. C. (2016). Comparative analysis of standby systems with unreliable server and switching failure. Reliability Engineering and System Safety, 145, 74–82. https://doi.org/10.1016/j.ress.2015.09.001
    101. Lee, D., & Pan, R. (2017). Predictive maintenance of complex system with multilevel reliability structure. International Journal of Production Research, 55(16), 4785–4801. https://doi.org/10.1080/00207543.2017.1299947
    102. Lee, D., & Pan, R. (2017). Predictive maintenance of complex system with multilevel reliability structure. International Journal of Production Research, 55(16), 4785–4801. https://doi.org/10.1080/00207543.2017.1299947
    103. Lee, H., & Cha, J. H. (2016). New stochastic models for preventive maintenance and maintenance optimization. European Journal of Operational Research, 255(1), 80–90. https://doi.org/10.1016/j.ejor.2016.04.020
    104. Lee, H., & Cha, J. H. (2016). New stochastic models for preventive maintenance and maintenance optimization. European Journal of Operational Research, 255(1), 80–90. https://doi.org/10.1016/j.ejor.2016.04.020
    105. Lee, Y. (2017). Comments on “Comparative analysis of standby systems with unreliable server and switching failure” [Reliab Eng Syst Saf 2016; 145: 74–82]. Reliability Engineering and System Safety, 160, 98–100. https://doi.org/10.1016/j.ress.2016.11.005
    106. Lee, Y. (2017). Comments on “Comparative analysis of standby systems with unreliable server and switching failure” [Reliab Eng Syst Saf 2016; 145: 74–82]. Reliability Engineering and System Safety, 160, 98–100. https://doi.org/10.1016/j.ress.2016.11.005
    107. Leigh, J., & Dunnett, S. (2016). Use of Petri nets to model the maintenance of wind turbines. Wind Energy Systems, 1–30.
    108. Leigh, J., & Dunnett, S. (2016). Use of Petri nets to model the maintenance of wind turbines. Wind Energy Systems, 1–30.
    109. Levitin, G., Xing, L., & Huang, H. Z. (2019). Dynamic availability and performance deficiency of common bus systems with imperfectly repairable components. Reliability Engineering and System Safety, 189(March), 58–66. https://doi.org/10.1016/j.ress.2019.04.007
    110. Levitin, G., Xing, L., & Huang, H. Z. (2019). Dynamic availability and performance deficiency of common bus systems with imperfectly repairable components. Reliability Engineering and System Safety, 189(March), 58–66. https://doi.org/10.1016/j.ress.2019.04.007
    111. Li, S., Yang, Z., Tian, H., Chen, C., Zhu, Y., Deng, F., & Lu, S. (2021). Failure analysis for hydraulic system of heavy-duty machine tool with incomplete failure data. Applied Sciences (Switzerland), 11(3), 1–18. https://doi.org/10.3390/app11031249
    112. Li, S., Yang, Z., Tian, H., Chen, C., Zhu, Y., Deng, F., & Lu, S. (2021). Failure analysis for hydraulic system of heavy-duty machine tool with incomplete failure data. Applied Sciences (Switzerland), 11(3), 1–18. https://doi.org/10.3390/app11031249
    113. Liang, Q., Yang, Y., Zhang, H., Peng, C., & Lu, J. (2022). Analysis of simplification in Markov state-based models for reliability assessment of complex safety systems. Reliability Engineering & System Safety, 221, 108373. https://doi.org/10.1016/j.ress.2022.108373
    114. Liang, Q., Yang, Y., Zhang, H., Peng, C., & Lu, J. (2022). Analysis of simplification in Markov state-based models for reliability assessment of complex safety systems. Reliability Engineering & System Safety, 221, 108373. https://doi.org/10.1016/j.ress.2022.108373
    115. Lindén, J., Sellgren, U., & Söderberg, A. (2016). Model-based reliability analysis. Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM, 30(3), 277–288. https://doi.org/10.1017/S0890060416000251
    116. Lindén, J., Sellgren, U., & Söderberg, A. (2016). Model-based reliability analysis. Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM, 30(3), 277–288. https://doi.org/10.1017/S0890060416000251
    117. Lipol, L. S., & Haq, J. (2011). Risk analysis method: FMEA/FMECA in the organizations. International Journal of Basic & Applied Sciences, 11(5), 74–82.
    118. Lipol, L. S., & Haq, J. (2011). Risk analysis method: FMEA/FMECA in the organizations. International Journal of Basic & Applied Sciences, 11(5), 74–82.
    119. Lisnianski, A., Levit, E., & Teper, L. (2021). Short-term availability and performability analysis for a large-scale multistate system based on robotic sensors. Reliability Engineering and System Safety, 205(October 2019), 107206. https://doi.org/10.1016/j.ress.2020.107206
    120. Lisnianski, A., Levit, E., & Teper, L. (2021). Short-term availability and performability analysis for a large-scale multistate system based on robotic sensors. Reliability Engineering and System Safety, 205(October 2019), 107206. https://doi.org/10.1016/j.ress.2020.107206
    121. Liu, B., Wen, Y., Qiu, Q., Shi, H., & Chen, J. (2022). Reliability analysis for multistate systems under K-mixed redundancy strategy considering switching failure. Reliability Engineering & System Safety, 228, 108814. https://doi.org/10.1016/j.ress.2022.108814
    122. Liu, B., Wen, Y., Qiu, Q., Shi, H., & Chen, J. (2022). Reliability analysis for multistate systems under K-mixed redundancy strategy considering switching failure. Reliability Engineering & System Safety, 228, 108814. https://doi.org/10.1016/j.ress.2022.108814
    123. Liu, J., Zhuang, X., & Pang, H. (2022). Reliability and hybrid maintenance modeling for competing failure systems with multistage periods. Probabilistic Engineering Mechanics, 68, 103254. https://doi.org/10.1016/j.probengmech.2022.103254
    124. Liu, J., Zhuang, X., & Pang, H. (2022). Reliability and hybrid maintenance modeling for competing failure systems with multistage periods. Probabilistic Engineering Mechanics, 68, 103254. https://doi.org/10.1016/j.probengmech.2022.103254
    125. Liu, W., Li, A., Fang, W., Love, P. E. D., Hartmann, T., & Luo, H. (2023). A hybrid data-driven model for geotechnical reliability analysis. Reliability Engineering & System Safety, 231, 108985. https://doi.org/10.1016/j.ress.2022.108985
    126. Liu, W., Li, A., Fang, W., Love, P. E. D., Hartmann, T., & Luo, H. (2023). A hybrid data-driven model for geotechnical reliability analysis. Reliability Engineering & System Safety, 231, 108985. https://doi.org/10.1016/j.ress.2022.108985
    127. Long, F., Zeiler, P., & Bertsche, B. (2016). Modelling the production systems in industry 4.0 and their availability with high-level Petri nets. IFAC-PapersOnLine, 49(12), 145–150. https://doi.org/10.1016/j.ifacol.2016.07.565
    128. Long, F., Zeiler, P., & Bertsche, B. (2016). Modelling the production systems in industry 4.0 and their availability with high-level Petri nets. IFAC-PapersOnLine, 49(12), 145–150. https://doi.org/10.1016/j.ifacol.2016.07.565
    129. Lopez, J. C., & Kolios, A. (2022). Risk-based maintenance strategy selection for wind turbine composite blades. Energy Reports, 8, 5541–5561. https://doi.org/10.1016/j.egyr.2022.04.027
    130. Lopez, J. C., & Kolios, A. (2022). Risk-based maintenance strategy selection for wind turbine composite blades. Energy Reports, 8, 5541–5561. https://doi.org/10.1016/j.egyr.2022.04.027
    131. Lu, J., Wu, X., Liu, Y., & Ann, M. (2015). Reliability analysis of large phased-mission systems with repairable components based on success-state sampling. Reliability Engineering and System Safety, 142, 123–133. https://doi.org/10.1016/j.ress.2015.05.010
    132. Lu, J., Wu, X., Liu, Y., & Ann, M. (2015). Reliability analysis of large phased-mission systems with repairable components based on success-state sampling. Reliability Engineering and System Safety, 142, 123–133. https://doi.org/10.1016/j.ress.2015.05.010
    133. Luo, M., Yan, H. C., Hu, B., Zhou, J. H., & Pang, C. K. (2015). A data-driven two-stage maintenance framework for degradation prediction in semiconductor manufacturing industries. Computers and Industrial Engineering, 85, 414–422. https://doi.org/10.1016/j.cie.2015.04.008
    134. Luo, M., Yan, H. C., Hu, B., Zhou, J. H., & Pang, C. K. (2015). A data-driven two-stage maintenance framework for degradation prediction in semiconductor manufacturing industries. Computers and Industrial Engineering, 85, 414–422. https://doi.org/10.1016/j.cie.2015.04.008
    135. Saad, N., & Arrofiq, M. (2012). A PLC-based modified-fuzzy controller for PWM-driven induction motor drive with constant V/Hz ratio control. Robotics and Computer-Integrated Manufacturing, 28(2), 95–112. https://doi.org/10.1016/j.rcim.2011.07.001
    136. Safaei, N., Banjevic, D., & Jardine, A. K. S. (2010). Impact of the use-based maintenance policy on the performance of cellular manufacturing systems. International Journal of Production Research, 48(8), 2233–2260. https://doi.org/10.1080/00207540802710273
    137. Sajaradj, Z., Huda, L. N., & Sinulingga, S. (2019). The application of reliability centered maintenance (RCM) methods to design maintenance system in manufacturing (Journal Review). IOP Conference Series: Materials Science and Engineering, 505(1). https://doi.org/10.1088/1757-899X/505/1/012058
    138. Sakurahara, T., O’Shea, N., Cheng, W. C., Zhang, S., Reihani, S., Kee, E., & Mohaghegh, Z. (2019). Integrating renewal process modeling with probabilistic physics-of-failure: Application to Loss of Coolant Accident (LOCA) frequency estimations in nuclear power plants. Reliability Engineering and System Safety, 190(December 2018), 106479. https://doi.org/10.1016/j.ress.2019.04.032
    139. Saleh, A., Chiachío, M., Salas, J. F., & Kolios, A. (2023). Self-adaptive optimized maintenance of offshore wind turbines by intelligent Petri nets. Reliability Engineering and System Safety, 231(September 2022), 109013. https://doi.org/10.1016/j.ress.2022.109013
    140. Sarkar, A., Chandra Panja, S., & Sarkar, B. (2011). Survey of maintenance policies for the last 50 years. International Journal of Software Engineering & Applications, 2(3), 130–148. https://doi.org/10.5121/ijsea.2011.2310
    141. Scheu, M. N., Tremps, L., Smolka, U., Kolios, A., & Brennan, F. (2019). A systematic Failure Mode Effects and Criticality Analysis for offshore wind turbine systems towards integrated condition-based maintenance strategies. Ocean Engineering, 176(March), 118–133. https://doi.org/10.1016/j.oceaneng.2019.02.048
    142. Shafiee, M. (2015). Maintenance strategy selection problem: An MCDM overview. Journal of Quality in Maintenance Engineering, 21(4), 378–402. https://doi.org/10.1108/JQME-09-2013-0063
    143. Shao, J., Lu, F., Zeng, C., & Xu, M. (2014). The principle and application of physics-of-failure based reliability technology. In Proceedings of 2014 Prognostics and System Health Management Conference, PHM 2014 (pp. 75–78). https://doi.org/10.1109/PHM.2014.6988136
    144. Sharma, K. D., & Srivastava, S. (2018). Failure Mode and Effect Analysis (FMEA) implementation: A literature review. Journal of Advance Research in Aeronautics and Space Science, 5(2), 2454–8669.
    145. Sheu, S. H., & Chang, C. C. (2010). Extended periodic imperfect preventive maintenance model of a system subjected to shocks. International Journal of Systems Science, 41(10), 1145–1153. https://doi.org/10.1080/00207720902974652
    146. Shi, H., & Zeng, J. (2016). Real-time prediction of remaining useful life and preventive opportunistic maintenance strategy for multi-component systems considering stochastic dependence. Computers and Industrial Engineering, 93, 192–204. https://doi.org/10.1016/j.cie.2015.12.016
    147. Shin, S.-M., Lee, S. H., Shin, S. K., Jang, I., & Park, J. (2021). STPA-based hazard and importance analysis on NPP safety I&C systems focusing on human–system interactions. Reliability Engineering and System Safety, 213, 107698. https://doi.org/10.1016/j.ress.2021.107698
    148. Sifonte, J. R., & Reyes-Picknell, J. V. (2017). Reliability centered maintenance-reengineered. In Reliability centered maintenance-reengineered. https://doi.org/10.1201/9781315207179
    149. Signoret, J. P., Dutuit, Y., Cacheux, P. J., Folleau, C., Collas, S., & Thomas, P. (2013). Make your Petri nets understandable: Reliability block diagrams driven Petri nets. Reliability Engineering and System Safety, 113(1), 61–75. https://doi.org/10.1016/j.ress.2012.12.008
    150. Singh, K., & Jagannath, S. (2016). Defects reduction using root cause analysis approach in gloves manufacturing unit. International Research Journal of Engineering and Technology, 3(7), 173–183. https://www.irjet.net/archives/V3/i7/IRJET-V3I733.pdf
    151. Skima, H., Medjaher, K., Varnier, C., Dedu, E., & Bourgeois, J. (2016). A hybrid prognostics approach for MEMS: From real measurements to remaining useful life estimation. Microelectronics Reliability. https://doi.org/10.1016/j.microrel.2016.07.142
    152. Slimani, M., Butzen, P., Naviner, L., Wang, Y., & Cai, H. (2016). Reliability analysis of hybrid spin transfer torque magnetic tunnel junction/CMOS majority voters. Microelectronics Reliability, 6-11. https://doi.org/10.1016/j.microrel.2016.07.074
    153. Soares, I. A., Ylipää, T., Gullander, P., Bokrantz, J., & Skoogh, A. (2022). Prioritisation of root cause analysis in production disturbance management. International Journal of Quality and Reliability Management, 39(5), 1133–1150. https://doi.org/10.1108/IJQRM-12-2020-0402
    154. Song, Y., Liu, D., Yang, C., & Peng, Y. (2017). Data-driven hybrid remaining useful life estimation approach for spacecraft lithium-ion battery. Microelectronics Reliability. https://doi.org/10.1016/j.microrel.2017.06.045
    155. Spreafico, C., Russo, D., & Rizzi, C. (2017). A state-of-the-art review of FMEA/FMECA including patents. Computer Science Review, 25, 19–28. https://doi.org/10.1016/j.cosrev.2017.05.002
    156. Squiller, D., Greve, H., Mengotti, E., & McCluskey, F. P. (2014). Physics-of-failure assessment methodology for power electronic systems. Microelectronics Reliability, 54(9–10), 1680–1685. https://doi.org/10.1016/j.microrel.2014.07.123
    157. Sulaman, S. M., Abbas, T., Wnuk, K., & Höst, M. (2014). Hazard analysis of collision avoidance system using STPA. In ISCRAM 2014 Conference Proceedings - 11th International Conference on Information Systems for Crisis Response and Management (pp. 424–428).
    158. Takeda Berger, S. L., Zanella, R. M., & Frazzon, E. M. (2019). Toward a data-driven predictive-reactive production scheduling approach based on inventory availability. IFAC-PapersOnLine, 52(13), 1343–1348. https://doi.org/10.1016/j.ifacol.2019.11.385
    159. Talebberrouane, M., Khan, F., & Lounis, Z. (2016). Availability analysis of safety critical systems using advanced fault tree and stochastic Petri net formalisms. Journal of Loss Prevention in the Process Industries, 44, 193–203. https://doi.org/10.1016/j.jlp.2016.09.007
    160. Tamer, M. E.-D., Ahmed, M. E.-A., Islam, H. A.-A., & Ahmed, A. E.-B. (2016). Implementation of FMECA and Fishbone techniques in reliability centered maintenance planning. International Journal of Innovative Research in Science, Engineering and Technology, 5(11). https://doi.org/10.15680/IJIRSET.2016.0511001
    161. Temsamani, A. B., Kauffmann, S., Descas, Y., Vandevelde, B., Zanon, F., & Willems, G. (2017). Improved and accurate physics-of-failure (PoF) methodology for qualification and lifetime assessment of electronic systems. Microelectronics Reliability, 76–77, 42–46. https://doi.org/10.1016/j.microrel.2017.06.047
    162. Temsamani, A. B., Kauffmann, S., Helsen, S., Gaens, T., & Driesen, V. (2018). Physics-of-failure (PoF) methodology for qualification and lifetime assessment of supercapacitors for industrial applications. Microelectronics Reliability, 88–90(June), 54–60. https://doi.org/10.1016/j.microrel.2018.06.084
    163. Thangamani, G. (2012). Generalized stochastic Petri nets for reliability analysis of lube oil system with common-cause failures. American Journal of Computational and Applied Mathematics, 2(4), 152–158. https://doi.org/10.5923/j.ajcam.20120204.03
    164. Tinga, T. (2010). Application of physical failure models to enable usage and load based maintenance. Reliability Engineering and System Safety, 95(10), 1061–1075. https://doi.org/10.1016/j.ress.2010.04.015
    165. Torell, W., & Avelar, V. (2004). Mean time between failure: Explanation and standards. Power, 78(December), 1–10. http://support.casit.net/Portals/0/NTForums_Attach/VAVR-5WGTSB_R0_EN.pdf
    166. Trivedi, S. K., & Bobbio, A. (2017). Reliability and availability. In Cogeneration: Technologies, optimisation and implementation (pp. 179–189). https://doi.org/10.1049/pbpo087e_ch9
    167. Tuncay, D., & Demirel, N. (2017). Reliability analysis of a dragline using fault tree analysis. Madencilik, 56(2), 55–64
    168. Ullah, G., Bruno, W. J., & Pearson, J. E. (2012). Simplification of reversible Markov chains by removal of states with low equilibrium occupancy. Journal of Theoretical Biology, 311, 117–129. https://doi.org/10.1016/j.jtbi.2012.07.007
    169. Vanderley, de V., Soares, W. A., da Costa, A. C. L., & Raso, A. L. (2019). Use of reliability block diagram and fault tree techniques in reliability analysis of emergency diesel generators of nuclear power plants. International Journal of Mathematical, Engineering and Management Sciences, 4(4), 814–823. https://doi.org/10.33889/IJMEMS.2019.4.4-064
    170. Velmurugan, R. S., & Dhingra, T. (2015). Maintenance strategy selection and its impact in maintenance function: A conceptual framework. International Journal of Operations and Production Management, 35(12), 1622–1661. https://doi.org/10.1108/IJOPM-01-2014-0028
    171. Verlinden, S., Deconinck, G., & Coupé, B. (2012). Hybrid reliability model for nuclear reactor safety system. Reliability Engineering & System Safety, 101, 35–47. https://doi.org/10.1016/j.ress.2012.01.004
    172. Vismari, L. F., João, B. C. J., Antonio, V. da S. N., Ricardo, A. V. G., & Paulo, S. C. (2015). A practical analytical approach to increase confidence in software safety arguments. IEEE Systems Journal, 12(4), 3473–3484. https://doi.org/10.1109/JSYST.2017.2726178
    173. Viveros, P., Zio, E., Nikulin, C., Stegmaier, R., & Bravo, G. (2014). Resolving equipment failure causes by root cause analysis and theory of inventive problem solving. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 228(1), 93–111. https://doi.org/10.1177/1748006X13494775
    174. Wang, C., Qiu, J., Liu, G., & Zhang, Y. (2014). Testability evaluation using prior information of multiple sources. Chinese Journal of Aeronautics, 27(4), 867–874. https://doi.org/10.1016/j.cja.2014.03.029
    175. Wang, L., Wang, X., Xia, Y., Hong, T., Polytechnic, K., Hom, H., & Kong, H. (2013). Hybrid reliability analysis of structures with multisource uncertainties. Acta Mechanica, 225(2), 413–430. https://doi.org/10.1007/s00707-013-0969-0
    176. Wang, L., Xiong, C., & Yang, Y. (2018). A novel methodology of reliability-based multidisciplinary design. Computer Methods in Applied Mechanics and Engineering. https://doi.org/10.1016/j.cma.2018.04.003
    177. Wang, L., Yang, Q., & Tian, Y. (2017). Reliability analysis of 6-component star Markov repairable system with spatial dependence. Reliability Engineering & System Safety.
    178. Wang, P., Wang, S., Zhang, X., Zhao, X., Xiang, Z., & Guo, C. (2020). Simplified hybrid reliability simulation approach of a VSC DC grid with integration of an improved DC current flow controller. Microelectronics Reliability, 114(November), 113782. https://doi.org/10.1016/j.microrel.2020.113782
    179. Wang, W., Zhang, D., Cheng, G., & Shen, L. (2012). The dynamic fault tree analysis of not-cutting failure for MG550/1220 electrical haulage shearer. Applied Mechanics and Materials, 130–134, 646–649. https://doi.org/10.4028/www.scientific.net/AMM.130-134.646
    180. Wang, X., Di, P., & Ni, Z. (2019). An approach to testability evaluation based on improved D-S evidence theory. ACM International Conference Proceeding Series, 155–159. https://doi.org/10.1145/3357292.3357308
    181. Wang, Y., Deng, C., Wu, J., Wang, Y., & Xiong, Y. (2014). A corrective maintenance scheme for engineering equipment. Engineering Failure Analysis, 36, 269–283. https://doi.org/10.1016/j.engfailanal.2013.10.006
    182. Wang, Y., Xing, L., Wang, H., & Levitin, G. (2015). Combinatorial analysis of body sensor networks subject to probabilistic competing failures. Reliability Engineering and System Safety, 142, 388–398. https://doi.org/10.1016/j.ress.2015.06.005
    183. Wofuru, O. K., Tobinson, N., & Daniel, A. B. (2022). Advancements in sustainable manufacturing supply chain modelling: A review. Sustainability.
    184. Wu, D., Gao, W., & Gao, W. (2017). Hybrid uncertain static analysis with random and interval fields. Computer Methods in Applied Mechanics and Engineering, 315, 222–246. https://doi.org/10.1016/j.cma.2016.10.047
    185. Wu, X., & Wu, X.-Y. (2015). Extended object-oriented Petri net model for mission reliability simulation of repairable PMS with common cause failures. Reliability Engineering & System Safety, 136, 109–119. https://doi.org/10.1016/j.ress.2014.11.012
    186. Wu, X., Yu, H., & Balakrishnan, N. (2022). Modular model and algebraic phase algorithm for reliability modelling and evaluation of phased-mission systems with conflicting phase redundancy. Reliability Engineering & System Safety, 227, 108735. https://doi.org/10.1016/j.ress.2022.108735
    187. Xing, L., & Levitin, G. (2012). BDD-based reliability evaluation of phased-mission systems with internal/external common-cause failures. Reliability Engineering and System Safety. https://doi.org/10.1016/j.ress.2012.12.003
    188. Xing, L., Shrestha, A., & Dai, Y. (2011). Exact combinatorial reliability analysis of dynamic systems with sequence-dependent failures. Reliability Engineering and System Safety, 96(10), 1375–1385. https://doi.org/10.1016/j.ress.2011.05.007
    189. Yang, D. Y., & Tsao, C. L. (2019). Reliability and availability analysis of standby systems with working vacations and retrial of failed components. Reliability Engineering and System Safety, 182, 46–55. https://doi.org/10.1016/j.ress.2018.09.020
    190. Yang, F., Yue, Z., Li, L., & Guan, D. (2017). Hybrid reliability-based multidisciplinary design optimization with random and interval variables. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability. https://doi.org/10.1177/1748006X17736639
    191. Yang, J., Lian, G., Li, H., & Liang, B. (2018). Testability test program development research. Journal of Test and Evaluation, 6(4), 1–7.
    192. Yang, X., Liu, J., Chen, X., Qing, Q., & Wen, G. (2018). Hybrid structural reliability analysis under multisource uncertainties based on universal grey numbers. Shock and Vibration, 2018, 3529479. https://doi.org/10.1155/2018/3529479
    193. Yangyao, S., Xinchen, Z., Tianxiang, Y., & Zijian, Z. (2023). Multi-state balance system reliability research considering load influence. Reliability Engineering & System Safety, 233, 109087. https://doi.org/10.1016/j.ress.2023.109087
    194. Yen, T. C., Chen, W. L., & Chen, J. Y. (2016). Reliability and sensitivity analysis of the controllable repair system with warm standbys and working breakdown. Computers and Industrial Engineering, 97, 84–92. https://doi.org/10.1016/j.cie.2016.04.019
    195. Yılmaz, E., German, B. J., & Pritchett, A. R. (2023). Optimizing resource allocations to improve system reliability via the propagation of statistical moments through fault trees. Reliability Engineering & System Safety, 230, 108873. https://doi.org/10.1016/j.ress.2022.108873
    196. Yong, B., & Ba, Q. (2010). Subsea engineering handbook. Gulf Professional. www.huyett.com
    197. Yongle, H., Yifei, L., Fei, X., Binli, L., & Xin, T. (2020). Physics of failure of die-attach joints in IGBTs under accelerated aging: Evolution of micro-defects in lead-free solder alloys. Microelectronics Reliability, 109(February), 113637. https://doi.org/10.1016/j.microrel.2020.113637
    198. Yu, H., Can, N., Wang, Y., Wang, S., Ogbeyemi, A., & Zhang, W. (2022). An integrated approach to line balancing for a robotic production system with the unlimited availability of human resources. IFAC-PapersOnLine, 55(10), 1098–1103. https://doi.org/10.1016/j.ifacol.2022.09.536
    199. Zeller, M., & Montrone, F. (2018). Combination of component fault trees and Markov chains to analyze complex, software-controlled systems. 2018 2nd International Conference on System Reliability and Safety (ICSRS), 160–165. https://doi.org/10.1109/ICSRS.2018.8688854
    200. Zhang, E., Hou, L., Lu, G., Shi, X., Yu, J., & Wu, J. (2022). Model-and-data hybrid driven method for power system operational reliability evaluation with high penetration of renewable energy. Journal of Physics: Conference Series. https://doi.org/10.1088/1742-6596/2378/1/012007
    201. Zhang, X., Huang, K., Yan, P., & Lian, G. (2015). Hierarchical hybrid testability modeling and evaluation method based on information fusion. Journal of Systems Engineering and Electronics, 26(3), 523–532. https://doi.org/10.1109/JSEE.2015.00060
    202. Zheng, H., Kong, X., Xu, H., & Yang, J. (2021). Reliability analysis of products based on proportional hazard model with degradation trend and environmental factor. Reliability Engineering and System Safety, 216(March), 107964. https://doi.org/10.1016/j.ress.2021.107964
    203. Zheng, J., Okamura, H., Pang, T., & Dohi, T. (2021). Availability importance measures of components in smart electric power grid systems. Reliability Engineering and System Safety, 205(April 2020), 107164. https://doi.org/10.1016/j.ress.2020.107164
    204. Zheng, Q., Liu, X., Zhang, H., Gu, X., Fang, M., Wang, L., & Adeeb, S. (2021). Reliability evaluation method for pipes buried in fault areas based on the probabilistic fault displacement hazard analysis. Journal of Natural Gas Science and Engineering, 85, 103698. https://doi.org/10.1016/j.jngse.2020.103698
    205. Zhou, S., Ye, L., Xiong, S., & Xiang, J. (2022). Reliability analysis of dynamic fault trees with priority-AND gates based on irrelevance coverage model. Reliability Engineering & System Safety, 224, 108553. https://doi.org/10.1016/j.ress.2022.108553
    206. Zhu, S. P., Huang, H. Z., Peng, W., Wang, H. K., & Mahadevan, S. (2016). Probabilistic physics of failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty. Reliability Engineering and System Safety, 146, 1–12. https://doi.org/10.1016/j.ress.2015.10.002

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright (c) 2024 The Authors

How to cite

Obele, A. F., Aikhuele, D. O., Herold, N. U., Sorooshian, S., Ahadi, N., & Virutamasen, P. (2024). Reliability concerns of programmable logic controllers: Trends and methodologies from 2010-2023. Multidisciplinary Reviews, 7(12), 2024270. https://doi.org/10.31893/multirev.2024270
  • Article viewed - 256
  • PDF downloaded - 70